Turn On The Lights

Whenever a surveillance system is trying to smooth video,
light plays a very important role. Most security professionals
have acquired important video only to see that the resulting
footage is over or under exposed because the camera
is unable to adapt to the challenging light environment.
Trains are noted for being a particularly difficult surveillance environment and
several unique challenges arise and must be overcome in order to achieve consistently
smooth video footage. For example, a fast-moving train will often experience
severe light fluctuations when traveling through tunnels, open air and shade,
or when another train passes by. Yet these challenges are not limited to what is
happening outside of the train, but also include onboard challenges such as the
lights being switched on and off, and doors opening and closing. In order to ensure
clear imagery on trains, a tailor made solution is required.

TECHNOLOGY OVERVIEW AND THE CHALLENGES

Auto Exposure (AE) is a technology that has been around for many years. It adjusts
the shutter, IRIS, and gain based on the ambient light captured by the sensor.
The AE algorithm uses an image taken in ideal light conditions and stores it as a
reference image. If the current image brightness is higher or lower than the reference
image, the AE algorithm will adjust the current image to make it the same as
the reference image. However, if the change of brightness affects a small part of
the overall image, or if there is a significant light change in a small area, the traditional
AE algorithm will readjust the whole image, which is a problem that must be
addressed before the AE feature can be effectively deployed on trains.

Two of the most difficult challenges when using the AE function on trains are
caused by a combination of environmental and human factors. In a stable light
environment, the AE performs its function well but in the complex light environment
of a train, the standard AE algorithm cannot achieve the standards required.
Due to space restrictions onboard trains, most cameras that are deployed will be
compact, and are unable to accommodate an adjustable IRIS.

For this reason, the AE feature that we will consider in this article is related to
the shutter and gain.

A typical problem that is encountered on trains is when the AE algorithm compensates
for adjustments in light when it is not necessary. An example is when passengers
who are wearing light-colored clothing enter the carriage and increase the
brightness of the image causing the AE algorithm to reduce the image brightness.
If those passengers then leave the carriage, the AE algorithm will adjust the image
again because the image has become darker. A similar situation occurs when the
light conditions inside the carriage change due to the train entering or exiting a
tunnel or passing by some buildings that block out the light. The challenge of the
current AE algorithm is to keep it functioning as it was intended, but also to avoid
any unnecessary adjustments.

SOLUTION FOR THE PASSENGER

First, light conditions are very stable in cars with a sustained light source. These
cars will not experience significant light fluctuations internally as the train moves.
As a result, the AE algorithm does not need to be that sensitive to the light fluctuations
that occur outside the train.

Another factor that must be considered is whether a light change within a
small part of the image should be ignored. In general, the AE function will compensate
when there is a change in the lighting environment. Most AE algorithms
are designed to modify the image based on the overall image change. However, as the light source is generally quite
stable on a train, a slight change in
brightness would be attributed to a
change in the objects present in the
scene and would not trigger the AE
adjustment for this scenario. The AE
feature would still compensate if the
image brightness changed considerably
to ensure clear imagery.

We tested two AE algorithms by
installing two cameras in a car consist
and capturing video as the train entered
a tunnel. The first incorporates
a traditional AE algorithm while the
second has a new AE algorithm developed
by Moxa.

TAMPERING DETECTION FUNCTION

Fluctuating light conditions not only
affect image quality, but also impact
some functions that help enhance security.
The tampering function has traditionally
been unstable when deployed
in onboard environments. However,
this is a problem that must be overcome
for users who require excellent video
quality and a reliable security system
installed in their onboard environment.

The camera tampering feature automatically
detects when a camera is being
tampered with and issues an alert
to the user. This feature performs a
comparative analysis based on a digital
reference image taken by the camera
during a period of ideal brightness. The
amount of time from detecting a possible
event to issuing an alert can be split
into three phases. First, there is a learning
stage when a possible tampering
event is detected. Second, is the detection
stage, when the algorithm continues
to detect if there is any significant
image change based on the reference
image and also uses the new image to
upgrade the detection sample. Finally,
once a large change has occurred, the
function will trigger the event alarm.

In order for the tampering algorithm
to accurately determine whether
a camera is being tampered with, a
ratio needs to be established between
the expected amount of change to the
scene the camera would typically experience
onboard a train and when the
camera is actually being tampered with.

As onboard trains are constantly
changing environments a clear challenge
presents itself if the user wants to
deploy accurate tampering algorithms
onboard a train. Changes to image
brightness can be caused by many different
factors, and users do not want
to receive a tampering alert for normal
occurrences such as the train entering a
tunnel or passengers standing in front
of the camera. In some scenarios, it is
difficult for the algorithm to differentiate
between when actual tampering of
the camera is taking place and a normal
image change onboard a train occurs.

CHALLENGES FOR DEPLOYMENT

It is important that the tampering algorithm
judges correctly if it should or
should not send an alert. False alerts are when a normal event triggers the
alarm, and missed alerts are when a
real tampering event occurs but the
camera fails to send an alert. Several
challenges need to be overcome so that
users can avoid these two troublesome
scenarios.

When a camera is being tampered
with the color of the scene will
change; therefore, a change of color
should be counted as one of the
factors for triggering the tampering
alarm. Due to passenger movement
or when the train moves through different
environments the scene on a
train will experience a change in colors.
In either case, the camera’s algorithm
will send a false alert because
it believes that a tampering event is
taking place.

Another factor to decide whether
to trigger the tampering alarm or
not is when a small area of a scene
undergoes significant light change.
However, in a very simple or a very
complex environment, it might be
difficult to judge if this is an actual
tampering event.

Different scenarios require different
parameters for triggering the tampering
alarm. For example, frequent and
large changes to the image should be
expected in a crowded car. However,
there will be places on the train where
the image is more stable. Thus, the
feature needs to be flexible in order
to cope with different situations.
After considering the three challenges
above, an effective algorithm
will be able to judge whether a camera
is being tampered with as well as a human
operator could judge. In order to
increase the accuracy of the tampering
function, below are some solutions to
the aforementioned challenges.

HOW THE CHALLENGES
CAN BE OVERCOME

A variety of factors must be considered
to determine whether the camera is being
tampered with or not. The camera
will not only consider the change in
brightness of the image, but also the
contrast and other relevant factors.

Several contributing factors allow
the camera to get as close to a human’s
judgement as possible. The sensitivity
level of different parts of the scene can
be fine-tuned to better suit different environments,
as shown in Fig. 5 below.
The algorithm can consider overall
image changes and also partial image
changes at the same time. The tampering
alarm should only be triggered if
the number of partial changes is sufficient
to influence the overall change.

Maintaining excellent image quality
onboard trains is not a simple task.
Several measures and countermeasures
need to be considered to meet
fluctuating light conditions, and to
ensure that the tampering alarm is not
triggered accidentally and still functions
properly when
a camera tampering
event occurs.

This article originally appeared in the November 2016 issue of Security Today.